IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
Featured Guides
Data Science Roadmap 2026: A Complete Step-by-Step Guide
A data science roadmap in dependency order, from Python and SQL through cleaning, statistics and honest model evaluation to communicating results, with an exit test at every stage and one dataset that carries through the whole path.
What Is Generative AI? A Complete Beginner's Guide
What generative AI is, in plain language: how language models produce text one token at a time (shown with a tiny runnable model), how chat models are trained, how image models differ, what the technology is reliable for, and why it confabulates.
Learn Python from Scratch: A Complete 2026 Roadmap
How to learn Python from zero: install a current version (including the new Windows install manager), then work through seven stages, from values and loops to files, virtual environments, classes and tests, each ending with a small program you should be able to write on your own.
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How to Stand Out in a Crowded Junior Job Market
Youth unemployment in India's cities is nearly three times the adult rate, and junior tech roles draw many similar applicants. Official labour data and employer surveys show where the competition is and how screening has shifted. This guide turns that into five concrete ways for a junior candidate to stand out.
How to Audit a Messy Dataset Before Analysis
How to audit a dataset before analysis, with a reusable profiling function and two real examples: sessions data with missing months, inconsistent labels, duplicates and impossible values, and a scientific series whose negative numbers mean 'not available'. Plus a checklist by data quality dimension.
Building a 30-60-90 Day Plan for a New Data Job
Research on onboarding suggests new employees have about 90 days to prove themselves, and that clarity about the role, confidence and relationships drive success. This guide turns that into a 30-60-90 day plan for data analysts, scientists and engineers, with goals for each phase and an example you can adapt.
Cloud Run vs Cloud Functions vs App Engine
Google Cloud has three ways to run code without managing servers, and they have converged: functions are now deployed on Cloud Run, and Google recommends Cloud Run over App Engine for new work. This guide explains what each is today, where Cloud Run jobs fit for data work, what scaling to zero saves and how to choose.
Model Registry: Managing the ML Model Lifecycle
A model registry is where trained models become managed releases. This guide runs a full lifecycle in MLflow: a production champion, a retrained challenger, a validation gate on held-out data, promotion by moving an alias, tags for audit, and a one-line rollback, with the reasoning behind each step.
Event-Based Triggers with Azure Event Grid and ADF
Event-based triggers start an Azure Data Factory pipeline when something happens, not when a clock says so. This guide explains storage event and custom event triggers, the role of Azure Event Grid, path filters and their limits, passing the file name to the pipeline, and designing for duplicates and partial files.